Dual-energy CT was put forward in the 1970s. With the development of X-ray detectors and imaging systems, dual-energy CT is widely used. In recent years, with the development of technologies related to detectors and further demands for CT imaging, spectral CT using dual-energy or multi-energy (i.e., X-ray with two and more energy spectrums is employed to pass through objects to form signals for imaging, which is generally referred to as X-ray of a plurality of energy windows or energy channels) attracts widespread attention and flourishes in practical application. Compared with traditional mono-energy CT, the spectral CT not only gets rid of defects of spectrum hardening and insufficient contrast, but also can distinguish materials, especially materials with the same absorption coefficient at certain energies. These advantages make the spectral CT available for numerous clinical applications such as abdominal imaging and lung disease detection, etc.
In general, the spectral CT at each energy needs to collect complete CT data. Taking the fan-beam CT as an example, a complete projection dataset should cover a short-scan angle (180 degrees plus a fan-beam angle). The traditional multi-energy data collection requires a short scan at different energies for many times; or a dual-source or multi-source CT are employed to perform a single scan; or an energy resolution detector such as a photon-counting detector is employed to perform a single scan to acquire multi-energy projection data. The above solutions may encounter problems such as increased radiation dose, long scanning time and high hardware cost, etc.
One solution is to avoid collecting complete CT data. However, this solution is faced with a problem of inadequate projection data for reconstruction. For this reason, according to a general solution, compression sensing technologies are employed. That is, supposing the signal is sparse or becomes sparse in a transform domain, namely∥Ψ(x)∥0≤s  (1)
wherein x∈Rn denotes an original signal, Ψ(⋅) denotes a sparse operator, and s denotes sparseness. In practical application, generally L0 norm is replaced with L1 norm, which changes the problem into a convex programming problem, wherein A denotes a forward projection, and b denotes measured projection data:argminx ∥Ψ(x)∥l s.t. Ax=b  (2)
However, an improved spectral CT image reconstruction algorithm is still required.
The above-mentioned information disclosed in this Background section is only for the purpose of enhancing the understanding of background of the present disclosure and may therefore include information that does not constitute a prior art that is known to those of ordinary skill in the art.